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Rasch common-item equating is often used in high-stakes testing to maintain equivalent passing standards across test administrations. If unaddressed, item parameter drift poses a major threat to the accuracy of Rasch common-item equating. We compared the performance of well-established and newly developed drift detection methods in small and large sample sizes, varying the proportion of test items used as anchor (common) items and the proportion of drifted anchors. In the simulated-data study, the most accurate equating was obtained in large-sample conditions with a small-moderate number of drifted anchors using the mINFIT/mOUTFIT methods. However, when any drift was present in small-sample conditions and when a large number of drifted anchors were present in large-sample conditions, all methods performed ineffectively. In the operational-data study, percent-correct standards and failure rates varied across the methods in the large-sample exam but not in the small-sample exam. Different recommendations for high- and low-volume testing programs are provided.

In response to the COVID-19 pandemic, the American Board of Surgery (ABS) shifted from in-person to remote administrations of the oral certifying exam (CE). Although the overall exam architecture remains the same, there are a number of differences in administration and staffing costs, exam content, security concerns, and the tools used to give the exam. Overall, the remote oral CEs have been a success, being well-received by candidates and examiners, having comparable psychometric performance to the in-person exams, and allowing over 2,300 candidates to obtain board certification on time. Although the ABS will continue remote administrations in the near future, a permanent switch will require careful consideration of exam security, psychometrics, validity, examiner quality and engagement, and other issues related to comparability across administration modes.

Monoclonal antibodies with neutralizing activity against SARS-CoV-2 have demonstrated clinical benefits in cases of mild-to-moderate SARS-CoV-2 infection, substantially reducing the risk for hospitalization and severe disease.sup.1-4. Treatment generally requires the administration of high doses of these monoclonal antibodies and has limited efficacy in preventing disease complications or mortality among hospitalized patients with COVID-19.sup.5. Here we report the development and evaluation of anti-SARS-CoV-2 monoclonal antibodies with optimized Fc domains that show superior potency for prevention or treatment of COVID-19. Using several animal disease models of COVID-19.sup.6,7, we demonstrate that selective engagement of activating Fc[gamma] receptors results in improved efficacy in both preventing and treating disease-induced weight loss and mortality, significantly reducing the dose required to confer full protection against SARS-CoV-2 challenge and for treatment of pre-infected animals. Our results highlight the importance of Fc[gamma] receptor pathways in driving antibody-mediated antiviral immunity and exclude the possibility of pathogenic or disease-enhancing effects of Fc[gamma] receptor engagement of anti-SARS-CoV-2 antibodies upon infection. These findings have important implications for the development of Fc-engineered monoclonal antibodies with optimal Fc-effector function and improved clinical efficacy against COVID-19 disease. Optimization of monoclonal antibodies against SARS-CoV-2 for enhanced Fc-effector function increase their effectiveness for prevention and treatment of COVID-19 in multiple animal models of SARS-CoV-2 infection., Author(s): Rachel Yamin [sup.1] , Andrew T. Jones [sup.1] , Hans-Heinrich Hoffmann [sup.2] , Alexandra Schäfer [sup.3] , Kevin S. Kao [sup.1] , Rebecca L. Francis [sup.1] , Timothy P. [...]

Traditional psychometric guidelines suggest that at least several hundred respondents are needed to obtain accurate parameter estimates under the Rasch model. However, recent research indicates that Rasch equating results in accurate parameter estimates with sample sizes as small as 25. Item parameter drift under the Rasch model has been previously investigated, but not under small sample conditions. The current study simulated data using the Rasch model to investigate the impact of varying proportions, magnitudes, and directions of item drift across multiple administrations with sample sizes of 10, 25 and 50. High proportions of high magnitude drift strongly affected bias, RMSE, and classification accuracy. High proportions of positive drift (items becoming harder across administrations) were particularly problematic for classification accuracy, with accuracy falling to less than 60% in the most extreme conditions. Results indicate that item drift affects Rasch examinee ability estimates similarly under both small and large sample conditions, and even small to moderate degrees of unaddressed item drift can substantially affect the validity of scores and pass-fail decisions.

Studies investigating invariance have often been limited to measurement or prediction invariance. Selection invariance, wherein the use of test scores for classification results in equivalent classification accuracy between groups, has received comparatively little attention in the psychometric literature. Previous research suggests that some form of selection bias (lack of selection invariance) will exist in most testing contexts, where classification decisions are made, even when meeting the conditions of measurement invariance. We define this conflict between measurement and selection invariance as the invariance paradox. Previous research has found test reliability to be an important factor in minimizing selection bias. This study demonstrates that the location of maximum test information may be a more important factor than overall test reliability in minimizing decision errors between groups.

Practitioners often depend on item analysis to select items for exam forms and have a variety of options available to them. These include the point-biserial correlation, the agreement statistic, the B index, and the phi coefficient. Although research has demonstrated that these statistics can be useful for item selection, no research as of yet has compared the difficulty of the items that these statistics will select in test development. The purpose of this research was to compare the difficulty of the items that these statistics would select for a test form and how the difficulty of item selection might vary, depending on the cut score. Results showed that considerable variability existed between the difficulty levels of the items selected by each of the statistics. The difference in difficulty depended on the location of the cut score. The agreement statistic tended to select the easiest items, whereas the point-biserial correlation selected items near the mean. Other results showed that the phi index tended to select items near the value of the cut score, whereas the B index followed this pattern to a lesser extent. (Contains 1 figure and 1 table.)